param_generator.py

import argparse
import random
import os

## definition of search area
min_batch_size = 16
max_batch_size = 1024
min_lr = 0.0001
max_lr = 0.5
min_dense_layers = 1
max_dense_layers = 5
min_conv_layers = 1
max_conv_layers = 3
min_num_filters = 2
max_num_filters = 32
min_num_units = 11
max_num_units = 128

## settings for all parameter sets
use_augmentation = True
num_epochs = 10


def write_parameter_file(
        job_number=1,
        num_epochs=20,
        batch_size=128,
        lr=0.01,
        num_units=30,
        dense_layers=1,
        num_filters=16,
        conv_layers=1,
        augment=False,
        path="./parameter/",
):
    if augment:
        augment_str = "--augment_data"
    else:
        augment_str = ""

    parameters = (
            "--num_epochs %d --batch_size %d --learning_rate %f"
            " --num_units %d --dense_layers %d --conv_layers %d"
            " --num_filters %d %s\n"
            % (
                # " --early_stopping --trainfile_suffix %d --num_filters %d %s\n" % (
                num_epochs,
                batch_size,
                lr,
                num_units,
                dense_layers,
                conv_layers,
                num_filters,
                augment_str,
            )
    )

    with open(path + '/' + str(job_number) + ".params", "w") as file:
        file.write(parameters)


def main():
    parser = argparse.ArgumentParser(
        description='generates parameters for a random parameterscan. The range of parameters to generate can be setup in the script')
    parser.add_argument("--output_dir", type=str, default="./parameter",
                        required=False, action='store',
                        help="The directroy, where the output should be stored")
    parser.add_argument("-n", type=int, default=5,
                        required=False, action='store',
                        help="Number of params to generate")
    ARGS = parser.parse_args()
    os.makedirs(ARGS.output_dir, exist_ok=True)
    # use range +1 will lead to less errors when setting the slurm array boundary
    for i in range(ARGS.n + 1):
        # check random parameters
        write_parameter_file(
            job_number=i,
            num_epochs=num_epochs,
            batch_size=random.randint(min_batch_size, max_batch_size),
            lr=random.uniform(min_lr, max_lr),
            num_units=random.randint(min_num_units, max_num_units),
            dense_layers=random.randint(min_dense_layers, max_dense_layers),
            num_filters=random.randint(min_num_filters, max_num_filters),
            conv_layers=random.randint(min_conv_layers, max_conv_layers),
            augment=use_augmentation,
            path=ARGS.output_dir
        )


if __name__ == "__main__":
    main()